Main¶

=============== <Original Dataset> ===============
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 10 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   longitude           20640 non-null  float64
 1   latitude            20640 non-null  float64
 2   housing_median_age  20640 non-null  float64
 3   total_rooms         20640 non-null  float64
 4   total_bedrooms      20433 non-null  float64
 5   population          20640 non-null  float64
 6   households          20640 non-null  float64
 7   median_income       20640 non-null  float64
 8   median_house_value  20640 non-null  float64
 9   ocean_proximity     20640 non-null  object 
dtypes: float64(9), object(1)
memory usage: 1.6+ MB
None

longitude latitude housing_median_age total_rooms total_bedrooms population households median_income median_house_value ocean_proximity
0 -122.23 37.88 41.0 880.0 129.0 322.0 126.0 8.3252 452600.0 NEAR BAY
1 -122.22 37.86 21.0 7099.0 1106.0 2401.0 1138.0 8.3014 358500.0 NEAR BAY
2 -122.24 37.85 52.0 1467.0 190.0 496.0 177.0 7.2574 352100.0 NEAR BAY
3 -122.25 37.85 52.0 1274.0 235.0 558.0 219.0 5.6431 341300.0 NEAR BAY
4 -122.25 37.85 52.0 1627.0 280.0 565.0 259.0 3.8462 342200.0 NEAR BAY
... ... ... ... ... ... ... ... ... ... ...
20635 -121.09 39.48 25.0 1665.0 374.0 845.0 330.0 1.5603 78100.0 INLAND
20636 -121.21 39.49 18.0 697.0 150.0 356.0 114.0 2.5568 77100.0 INLAND
20637 -121.22 39.43 17.0 2254.0 485.0 1007.0 433.0 1.7000 92300.0 INLAND
20638 -121.32 39.43 18.0 1860.0 409.0 741.0 349.0 1.8672 84700.0 INLAND
20639 -121.24 39.37 16.0 2785.0 616.0 1387.0 530.0 2.3886 89400.0 INLAND

20640 rows × 10 columns

=============== <Modified Dataset> ===============
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20433 entries, 0 to 20432
Data columns (total 9 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   longitude           20433 non-null  float64
 1   latitude            20433 non-null  float64
 2   housing_median_age  20433 non-null  float64
 3   total_rooms         20433 non-null  float64
 4   total_bedrooms      20433 non-null  float64
 5   population          20433 non-null  float64
 6   households          20433 non-null  float64
 7   median_income       20433 non-null  float64
 8   ocean_proximity     20433 non-null  object 
dtypes: float64(8), object(1)
memory usage: 1.4+ MB
None

longitude latitude housing_median_age total_rooms total_bedrooms population households median_income ocean_proximity
0 -122.23 37.88 41.0 880.0 129.0 322.0 126.0 8.3252 NEAR BAY
1 -122.22 37.86 21.0 7099.0 1106.0 2401.0 1138.0 8.3014 NEAR BAY
2 -122.24 37.85 52.0 1467.0 190.0 496.0 177.0 7.2574 NEAR BAY
3 -122.25 37.85 52.0 1274.0 235.0 558.0 219.0 5.6431 NEAR BAY
4 -122.25 37.85 52.0 1627.0 280.0 565.0 259.0 3.8462 NEAR BAY
... ... ... ... ... ... ... ... ... ...
20428 -121.09 39.48 25.0 1665.0 374.0 845.0 330.0 1.5603 INLAND
20429 -121.21 39.49 18.0 697.0 150.0 356.0 114.0 2.5568 INLAND
20430 -121.22 39.43 17.0 2254.0 485.0 1007.0 433.0 1.7000 INLAND
20431 -121.32 39.43 18.0 1860.0 409.0 741.0 349.0 1.8672 INLAND
20432 -121.24 39.37 16.0 2785.0 616.0 1387.0 530.0 2.3886 INLAND

20433 rows × 9 columns

=============== AutoML Start ===============
=============== Model : DBSCAN ===============
min_samples = 100 / eps = 0.3 / metric = euclidean Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0    20433
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    179800.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    14999.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    207308.787696
Name: median_house_value, dtype: float64
min_samples = 100 / eps = 0.3 / metric = manhattan Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0    20433
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    179800.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    14999.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    207308.787696
Name: median_house_value, dtype: float64
min_samples = 200 / eps = 0.3 / metric = euclidean Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0    20433
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    179800.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    14999.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    207308.787696
Name: median_house_value, dtype: float64
min_samples = 200 / eps = 0.3 / metric = manhattan Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0    20433
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    179800.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    14999.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    207308.787696
Name: median_house_value, dtype: float64
min_samples = 100 / eps = 1 / metric = euclidean Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0     3293
 0.0     2346
 1.0    13618
 2.0     1176
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
 0.0    500001.0
 1.0    500001.0
 2.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    187500.0
 0.0    227800.0
 1.0    174300.0
 2.0    160150.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    14999.0
 0.0    22500.0
 1.0    14999.0
 2.0    40000.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    216074.700881
 0.0    246892.227195
 1.0    200529.627992
 2.0    182300.025510
Name: median_house_value, dtype: float64
min_samples = 100 / eps = 1 / metric = manhattan Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0    17576
 0.0      222
 1.0     2635
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
 0.0    500001.0
 1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    175750.0
 0.0    263200.0
 1.0    190000.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    14999.0
 0.0    55000.0
 1.0    38800.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    204108.153334
 0.0    287392.013514
 1.0    221910.637192
Name: median_house_value, dtype: float64
min_samples = 200 / eps = 1 / metric = euclidean Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0     5175
 0.0     2029
 1.0    12437
 2.0      792
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
 0.0    500001.0
 1.0    500001.0
 2.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    180700.0
 0.0    225000.0
 1.0    175000.0
 2.0    160250.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    14999.0
 0.0    22500.0
 1.0    14999.0
 2.0    40000.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    210905.057391
 0.0    244709.878265
 1.0    201351.750905
 2.0    181538.785354
Name: median_house_value, dtype: float64
min_samples = 200 / eps = 1 / metric = manhattan Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0    19257
 0.0     1176
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
 0.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    178100.0
 0.0    194900.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    14999.0
 0.0    47600.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    206309.705977
 0.0    223668.750850
Name: median_house_value, dtype: float64
min_samples = 100 / eps = 1.5 / metric = euclidean Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0      800
 0.0    19633
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
 0.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    190600.0
 0.0    179500.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    22500.0
 0.0    14999.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    220657.056250
 0.0    206764.876178
Name: median_house_value, dtype: float64
min_samples = 100 / eps = 1.5 / metric = manhattan Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0    10652
 0.0     1594
 1.0     1733
 2.0     6095
 3.0      359
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
 0.0    500001.0
 1.0    500001.0
 2.0    500001.0
 3.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    173150.0
 0.0    218400.0
 1.0    111800.0
 2.0    196200.0
 3.0    150000.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    14999.0
 0.0    22500.0
 1.0    27500.0
 2.0    17500.0
 3.0    40000.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    201712.594255
 0.0    239031.873275
 1.0    140027.188113
 2.0    229736.254799
 3.0    176522.309192
Name: median_house_value, dtype: float64
min_samples = 200 / eps = 1.5 / metric = euclidean Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0     1182
 0.0    19251
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
 0.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    193800.0
 0.0    179200.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    22500.0
 0.0    14999.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    219859.526227
 0.0    206538.179783
Name: median_house_value, dtype: float64
min_samples = 200 / eps = 1.5 / metric = manhattan Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
-1.0    14431
 0.0     1093
 1.0     4909
Name: median_house_value, dtype: int64
===max===
predict
-1.0    500001.0
 0.0    500001.0
 1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
-1.0    166900.0
 0.0    211600.0
 1.0    196100.0
Name: median_house_value, dtype: float64
===min===
predict
-1.0    14999.0
 0.0    22500.0
 1.0    38800.0
Name: median_house_value, dtype: float64
===mean===
predict
-1.0    197115.562955
 0.0    236359.169259
 1.0    230805.703402
Name: median_house_value, dtype: float64